AI Engineer (Artificial Intelligence Engineer)
The Rose
3 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
EnglishJob location
Remote
Tech stack
API
Artificial Intelligence
Amazon Web Services (AWS)
Computer Vision
Azure
Big Data
Data Cleansing
Hadoop
Python
Machine Learning
Natural Language Processing
NumPy
TensorFlow
Systems Architecture
Jupyter Notebook
Data Processing
Google Cloud Platform
Feature Engineering
PyTorch
Spark
Deep Learning
Generative AI
Pandas
Scikit Learn
Kubernetes
Machine Learning Operations
REST
Automation Anywhere
Docker
Job description
- Design and develop machine learning and AI models for various use cases
- Collect, preprocess, and analyze large datasets
- Train, test, and optimize machine learning models
- Deploy AI models into production environments
- Build and maintain data pipelines for AI workflows
- Integrate AI solutions with applications and APIs
- Monitor model performance and retrain models when required
- Collaborate with data scientists, engineers, and product teams
- Implement AI solutions following best practices for scalability and performance
- Document AI models, processes, and system architecture
Requirements
- Strong knowledge of Python and AI/ML libraries
- Understanding of machine learning algorithms and concepts
- Familiarity with data preprocessing and feature engineering
- Knowledge of deep learning frameworks (TensorFlow, PyTorch)
- Experience with data handling libraries (pandas, NumPy)
- Understanding of statistics and probability
- Problem-solving and analytical thinking skills, * Experience with Natural Language Processing (NLP) or Computer Vision
- Familiarity with Generative AI models and tools
- Knowledge of MLOps practices
- Experience with cloud platforms (AWS, Azure, Google Cloud Platform)
- Understanding of model deployment tools (Docker, Kubernetes)
- Exposure to big data technologies (Spark, Hadoop)
- Familiarity with REST APIs, * Languages: Python, R
- Frameworks: TensorFlow, PyTorch, Scikit-learn
- Libraries: pandas, NumPy
- Tools: Jupyter Notebook, VS Code
- Platforms: AWS, Azure, Google Cloud
- Others: Docker, Kubernetes
